garma.md
In garma: Fitting and Forecasting Gegenbauer ARMA Time Series Models

garma xreg

Code
  print(fit)
Output

  Call:
  garma(x = x, xreg = m)

  Mean term was fitted.
  No Drift (trend) term was fitted.


  Coefficients:
        intercept       R1         R2      u1      fd1
          0.45728  0.14706  -0.009936  0.2487  0.05572
  s.e.    0.05234  0.07003   0.069077  0.1066  0.04333

                          Factor1
  Gegenbauer frequency:    0.2100
  Gegenbauer Period:       4.7619
  Gegenbauer Exponent:     0.0557


  sigma^2 estimated as 0.0120: log likelihood = -1112.626391, aic = 2231.252783

Code
  predict(fit, n.ahead = 10, newdata = m.ahead)
Output
  $pred
  Time Series:
  Start = 201 
  End = 210 
  Frequency = 1 
          1         2         3         4         5         6         7         8 
  0.5549148 0.4559084 0.5172535 0.4832164 0.5877493 0.4816626 0.5738843 0.5097813 
          9        10 
  0.4972928 0.5312529